{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3992ce25",
   "metadata": {},
   "source": [
    "工龄和薪水之间存在一种复杂度非线性关系，工龄越长，薪水水平越高\n",
    "\n",
    "一共一百个样本， 包含两列数据， 分别代表不同工龄和对应月薪 \n",
    "\n",
    "![](http://cdn.sealight.site/notes/202510312109907.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d1d4d987",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.metrics import r2_score\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "# 读取数据集\n",
    "data = pd.read_excel(\"./IT income.xlsx\")\n",
    "X1 = data[[\"工龄\"]]\n",
    "X2 = data['工龄']\n",
    "Y = data[\"薪水\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "532f2feb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 二次曲线拟合数据\n",
    "poly = PolynomialFeatures(degree=2) # 设置最高次项为二次项\n",
    "X11 = poly.fit_transform(X1) # 把原有的X 转化为一个新的二维数组, 该二维数组包含新生成的二次项数据和原有的一次项数据\n",
    "model = LinearRegression()\n",
    "model.fit(X11,Y)\n",
    "r2 = r2_score(Y,model.predict(X11))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7eecf37e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 score:  0.9310387116075501\n",
      "模型参数：  [   0.    -743.681  400.804]\n"
     ]
    }
   ],
   "source": [
    "print(\"r2 score: \",r2)\n",
    "print(\"模型参数： \",np.round(model.coef_,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c84dd91",
   "metadata": {},
   "source": [
    "也就是： y = 400.804 x^2 - 743.681"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "97229821",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 幂函数拟合曲线\n",
    "\n",
    "def power_func(x,a,b):\n",
    "    return a*np.pow(x,b) # 这里我试了一下， 改成a*x*x+b*x+c， 算出来的结果和之前构造多项式然后用 LinearRegression 是一样的\n",
    "popt,pcov = curve_fit(power_func,X2,Y)\n",
    "r_square = r2_score(Y,power_func(X2,*popt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2bd1a05a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 score:  0.7056255717776865\n"
     ]
    }
   ],
   "source": [
    "print(\"r2 score: \",r_square)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "de86b019",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1146b5d10>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4. 绘制模型拟合图\n",
    "plt.rcParams['font.sans-serif'] = ['Songti SC']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "fig,axs = plt.subplots(1,2,figsize=(10,4))\n",
    "# 在第一张子图上绘制散点图和二次曲线拟合曲线\n",
    "axs[0].plot(X1,model.predict(X11),color='red',label=\"二次曲线\")\n",
    "axs[0].scatter(X1,Y,color='grey',label=\"数据\")\n",
    "axs[0].set_xlabel(\"工龄\")\n",
    "axs[0].set_ylabel(\"薪水\")\n",
    "axs[0].legend()\n",
    "\n",
    "# 在第二张子图上绘制幂函数拟合\n",
    "axs[1].plot(X2,power_func(X2,*popt),'r-',label=\"幂曲线\")\n",
    "axs[1].scatter(X1,Y,color='grey',label=\"数据\")\n",
    "axs[1].set_xlabel(\"工龄\")\n",
    "axs[1].set_ylabel(\"薪水\")\n",
    "axs[1].legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09950c64",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python-learn",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
